Among various longitudinal sensor streams, smartphones and wearables are arguably one of the most widely available data sources [1]}.
The advances in mobile technology provide an unprecedented opportunity to capture multiple aspects of daily human behaviors, by collecting continuous sensor streams from these devices [2]}, [3]}, together with metrics about health and well-being through self-report or clinical diagnosis as modeling targets.
It poses unique challenges compared to traditional time-series classification tasks [4]}.
First, the data covers a much longer time period, usually across multiple months or years.
Second, the nature of longitudinal collection often results in a high data missing rate.
Third, the prediction target label is sparse, especially for mental well-being metrics.
